There has been a great deal of research into methods for the automatic identification of digital representations. These methods are as diverse as the digital representations that are to be identified. For example, text recognition software utilizes methods involving the computation of discrete local symmetries; Microsoft's Kinect utilizes probabilistic tracking in metric spaces; and there is an entire plethora of skeletonization methodologies that utilize everything from voxelization of 3D spaces to Reb graphs.
The method utilized by Microsoft's Kinect device is by far the most widely used and impressive to date. However, Microsoft's probabilistic tracking method requires a large amount of training data in order to produce a predictive model, and each new predictive model requires substantial, even international, efforts to construct. This will not work for many industries that wish to dynamically and automatically identify real world objects because they require a method that can completely recognize an object after one scan, and then be able to match said identity to any other instances of the object. Other industries that can benefit from our technology are not even interested in recognizing objects, but are instead interested in being able to completely analyze the object based off of a single representation, such as a CAD design, and then be able to draw conclusions from said analysis.
Voxelization, Reb graphs, and energy gradient methods do retain this ability to perform analysis or identification based off of a single frame, or data set; however, they are known to be highly error prone and pose specific. This means that it is difficult to ensure that an object will be accurately and uniquely identified from instance to instance. Similarly, if an object is identified in one instance, and then repositioned, then the voxelization, Reb graph, and energy gradient methods may not recognize the re-positioned real world object as the same object from the initial data acquisition.